# -*- coding: utf-8 -*-

''' Autor: Neuza Figueira - n.º 6036 '''

from random import uniform
from time import clock
import matplotlib.pyplot as plt

class InsertionSort():
        ''' Class to handle Insertion sort implementation and testing.'''        
        
        def insertion_sort(self, A):
                '''Python Implementation of Insertion sort.'''
                '''@param A -> List to sort.'''
                for j in range(0, len(A)):
                        key = A[j]
                        i = j - 1
                        
                        while i >= 0 and A[i] > key:
                                A[i + 1] = A[i]
                                i = i - 1
                                A[i + 1] = key
                                
                return A


        def testInsertionSort(self):
                '''Testing the complexity of Insertion sort.'''
                Z = range(50, 1050, 50)
                T = []
                M = 50

                for n in Z:
                    A = [ uniform(0.0, 1.0) for k in xrange(n)]
                    tempos = []
                    
                    for k in range(M):
                        t1 = clock()
                        self.insertion_sort(A)
                        t2 = clock()
                        tempos.append(t2-t1)
                        
                    media = reduce(lambda x, y: x + y, tempos) / len(tempos)
                    var = reduce(lambda x, y: x + (y-media)**2, [0] + tempos) / len(tempos)
                    
                    T.append((n,media, var))

                X = [n for n, media, var in T]
                Y = [media for n, media, var in T]
                ct = 450e6
                Z = [(n**2) / ct for n, media, var in T]


                plt.grid(True)
                plt.ylabel(u'T(n) - tempo de execução médio em segundos')
                plt.xlabel(u'n - número de elementos')
                plt.plot(X,Y,'rs', label="resultado experimental")
                plt.plot(X, Z, 'b^', label=u"previsão teórica")
                plt.legend()
                plt.show()
                pass

